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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural computing and Applications 2025年第18期37卷 12991-13009页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Distributed Nonlinear Model Predictive Control of Heterogeneous Battery Storage systems 50

Distributed Nonlinear Model Predictive Control of Heterogene...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Vrbanc, Filip Car, Mateja Vašak, Mario Lešić, Vinko Laboratory for Renewable Energy Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665464543

This paper deals with distributed control of variable efficiency heterogeneous battery storage systems connected in a microgrid. The nonlinear efficiency of battery charging is taken from converters’ datasheet and identified battery internal resistance, and a corresponding nonlinear model is derived dependent on state of energy. Individual battery control, as a local nonlinear problem, is solved by using a sequential linear program. Distributed control with the central supervisor is then used to coordinate individual batteries within the storage system and ensure improved overall system operation while respecting the joint constraint of the grid connection power capacity. Asymmetric projection algorithm is used for distributed control, based on iterative convergence of individual battery control actions towards the optimal operation of the whole system. The algorithm is implemented in a realistic simulation scenario of six different batteries with their corresponding characteristics, real electricity prices and measured load consumption. Presented results show that distributed control satisfies global constraint, while obtaining better cost than the decentralized approach with previously reported 7% of increased savings of nonlinear over constant efficiency. © 2024 IEEE.

关键词： Predictive control systems

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Data-driven Predictive Control of Office Temperatures Based on Multiple Linear Regression Model 50

Data-driven Predictive Control of Office Temperatures Based ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Jakšić, Mihael Lešić, Vinko Laboratory for Renewable Energy Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665464543

The paper proposes a data-based approach in control of building temperature for the case of a single floor of an office building. Several obtained datasets are exploited to identify a mathematical model of building thermal dynamics. Input-output data mapping is performed by multiple linear regression to identify a linear model in form of a vector of coefficients related to historical variables of temperatures of the considered and neighboring offices, external variables and user behavior as disturbances. The model is exploited for temperature setpoint tracking optimal problem formulation and put as tradeoff of comfort and energy efficiency. Such data predictive control approach is implemented to a case study and realistic simulations are performed with real data, proving the soundness of the approach. The concept is motivated by easy replication of both hardware and software to be efficiently implemented in the buildings, driven by time-effectiveness of the proposed structure and identification method of the model. © 2024 IEEE.

关键词： Multiple linear regression

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Grid-Forming PV System for Frequency Ancillary Services Provision 3

Grid-Forming PV System for Frequency Ancillary Services Prov...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Barac, Bojana Capuder, Tomislav Panteli, Mathaios University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Croatia University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350375923

With the increasing penetration of photovoltaic (PV) systems posing challenges to power system stability, this paper investigates the effectiveness of grid-forming controlled PV systems in providing frequency ancillary services. The study implements the Virtual Synchronous Machine strategy, utilizing DC-link capacitor energy for inertial response provision (DCVQVSM), integrated within a grid-forming topology. To ensure power-frequency droop control, an additional control loop is introduced within the DCVQ-VSM topology. The efficacy of this control system is proved through simulations conducted on a simple two-bus system and a fully-electrified active distribution network (ADN). Both case studies demonstrate the successful operation of grid-forming controlled PV systems, highlighting their capability to influence power system frequency dynamics. The analysis underscores the significance of deloading PV operation in facilitating flexibility services provision, thereby enhancing the reliability and resilience of system operation. © 2024 IEEE.

关键词： Synchronous machinery

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Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

Annals of Emerging Technologies in Computing

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Annals of Emerging Technologies in computing 2024年第1期8卷 13-21页

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024 by the author(s).

关键词： Phase locked loops

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Intelligent Lightning Hazard Warning System for a Wind Farm

Intelligent Lightning Hazard Warning System for a Wind Farm

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Foroozan, Hossein Franc, Bojan Vašak, Mario Laboratory for Renewable Energy Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9798350332308

Wind energy is one of the most important forms of renewable energy, and with the progress in this field, as the production capacity of wind turbines has increased, their height has also increased significantly. The height of wind turbines, number of them in a wind farm, and their specific location have increased the probability of lightning strikes and made them one of the most important hazards for wind turbines. Given the importance of maintenance and inspection for wind farms, creating a system for detecting safe time for these operations with low lightning probability is very useful. In this regard, by analyzing local meteorological data (pressure, temperature, wind speed, wind direction and humidity) and the lightning location system data an intelligent warning system for lightning hazard in a wind farm is developed based on machine learning methods. It is applied and tested on a case study of a wind farm in Croatia. The results show the success of this lightning hazard warning system in predicting the safe times with low lightning probability for the wind farm. © 2023 IEEE.

关键词： Weather forecasting

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Reinforcement Learning for Joint Resource and Power Allocation in D2D Communications 63

Reinforcement Learning for Joint Resource and Power Allocati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Saeed, Ifrah Cullen, Andrew C. Zaidi, Zainab R. Erfani, Sarah Alpcan, Tansu The University of Melbourne Department of Electrical and Electronic Engineering Australia The University of Melbourne School of Computing and Information Systems Australia

ISBN: (纸本)9798350316339

Device-to-Device (D2D) communication is critical in many public safety scenarios where access to network infrastructure is not guaranteed. In such out-of-coverage situations, random resource allocation is a straightforward way for direct communication by each D2D pair. However, such a resource allocation plan often results in severe interference and low throughput, which can degrade crucial communications performance. In contrast, the problem of centralised joint resource block selection and power control optimisation to maximise the total data rate of all D2D pairs, known as the sum-rate, is non-convex and NP-hard but can effectively reduce interference. To address the inability to solve this non-convex centralised problem setting due to limited information in resource-constrained out-of-coverage scenarios, we propose two distributed reinforcement learning schemes. Our methods allow D2D pairs to autonomously make decisions on their joint resource block and power allocation to minimise their mutual interference and maximise their sum-rate, while maintaining the quality of service constraints. To evaluate these methods in out-of-coverage scenarios, we conduct extensive performance evaluations using the ns-3 network simulator designed for public safety LTE-D2D. We demonstrate that our algorithms, in the absence of any centralised coordination and neighbouring information, autonomously reach time-averaged sum-rates that are within 98.2 % and 98.6 % of the rates achieved by the centralised optimisation solution. © 2024 IEEE.

关键词： Reinforcement learning

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Power transmission network planning under uncertainties

Power transmission network planning under uncertainties

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Vukadinović, Neven Pavić, Ivica Holjevac, Ninoslav Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789531842976

The optimization of load flow analysis is crucial for the efficient observation of the impact of new power plants into existing electricity power systems (EPS). The advent of integration of new energy sources has reduced construction timeline requirements to 4-5 years, creating a lag in the development of supporting electricity infrastructure. This paper explores the spatial and temporal dimensions of EPS development planning, illustrated through the example of two new connection requests to a transmission network, with emphasis on importance of load flow optimization and the potential failure analysis to meet the (N-1) contingency criterion, which ensures system reliability during outages. This paper underscores the necessity of strategic planning in EPS development to accommodate new generation units while maintaining system stability and reliability at the satisfactory levels. © 2024 IEEE.

关键词： Load flow optimization

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Dual Distillation: A Lightweight and Effective Intrusion Detection Model for IoT Security 15

Dual Distillation: A Lightweight and Effective Intrusion Det...

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15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Pei, Chonghao Mhamdi, Lotfi University of Leeds School of Electronic and Electrical Engineering United Kingdom Abdullah Al Salem University College of Computing & Systems Kuwait

ISBN: (纸本)9798331520113

With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is proposed, combining data distillation and model distillation. Data distillation is achieved through KMeans clustering and refinement, while model distillation leverages a random forest (RF) teacher model to guide a simplified multi-layer perceptron (MLP) student model. The approach is validated on CICIDS2017 and TON-IoT datasets, where dataset sizes are reduced to 20% and 40%, respectively. Experimental results demonstrate that the proposed method enhances MLP accuracy on CICIDS2017 from 93.17% to 99.66% and on TON-IoT from 89.00% to 93.14%. Moreover, the method significantly reduces model parameters compared to the RF teacher model while maintaining competitive performance, making it highly suitable for resource-constrained IoT environments. © 2025 IEEE.

关键词： Network intrusion

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